Single cell transcriptional analysis has already demonstrated its ability to identify novel cell subsets but is currently limited by the number of cells and analytes that can be measured in parallel. We plan to increase both the number of cells and analytes that can be monitored by one to two orders of magnitude by using DNA-bar coded nanoliter well (nanowell) arrays to label each individual transcript with a DNA-encoded address. Next generation sequencing will identify both the transcript identity and the attached barcode, thereby tracing each sequenced transcript back to a single cell. This will be accomplished by transferring a million DNA barcodes synthesized on the surface of a microarray to primer-conjugated nanoparticles in the nanowells through asymmetric PCR while the nanowells are sealed by the microarray surface. The PCR reaction will also add a poly(dT) tail to each barcode. Single cells will then be sealed into the bar coded nanowells. Following lysis of the cells, the poly(dT) probe will capture the mRNA and reverse transcriptase will extend the poly(dT) sequence, thereby fusing the barcode with each transcript. The bar coded cDNA will be amplified and integrated into next generation sequencing workflows. The technique will be validated by comparing the measured transcript levels to the levels measured in the same cell population by single cell qPCR using the Fluidigm platform. We will also demonstrate that the barcodes identify individual cell transcripts by sequencing B and T cell receptor transcripts and demonstrating that each unique BCR or TCR transcript has a unique barcode fused to it and the barcode maps back to a well that originally contained the correct cell type. Furthermore, the single cell transcript data will be integrated with single cell secretion data from the same cells obtained prior to cell lysis through our previously described microengraving methodology, thereby establishing the first platform that can create highly multiplexed single cell transcript ad proteomic data from the same population of single cells. Application of this technology will greatly accelerate our understanding of single cell biology and heterogeneous cell populations. PUBLIC HEALTH RELEVANCE: Many important medical processes involve the interaction of a large number of distinct cell types that act together to produce an observed biological state. Traditional research approaches that average measurements over the entire population cannot determine the cell-to- cell interactions that are occurring between unique cell subsets within the population, which is critical for understanding the system as a whole and misses an opportunity for focusing drug discovery efforts on very specific cell types that are critical for the process. ur proposed method for highly parallel single cell transcriptome analysis will open the door to defining and understanding the biology of rare subsets of cells in heterogenous populations and thereby accelerate our understanding in fields as far ranging as immunology, cancer, stem cells and neurobiology.